About Dean Phillips

Dean Phillips is the Head of Product Strategy at ClickUp, where he leads the product direction for ClickUp Docs and the company’s emerging AI-powered Super Agents. Rising to prominence in the early 2020s, he became known for translating complex workflows into opinionated, high-leverage product systems inside a platform used by millions of users worldwide. At ClickUp, he currently oversees strategic initiatives that connect document creation, task management, and AI automation into a single unified experience.

Previously, as Head of Product Strategy and Product Manager at ClickUp, he helped scale the product organization through more than five years of rapid growth, serving in core product roles from December 2020 through at least early 2026. He became known for shipping multi-quarter initiatives that spanned Docs, collaboration surfaces, and workflow automation, partnering closely with engineering and data teams to improve productivity at scale. Before ClickUp, he served as Chief Technology Officer at The Pattern Trader, where over four and a half years he led full-stack product and technology efforts for a trading and analytics platform.

Hey, Thanks for reading this. I mean that. There's a lot of content out there competing for your attention, and you spent some of it here. I hope it was worth it. Even better, I hope it prompted you to think about something differently enough that you'd share it with someone who'd get something out of it too.I started this podcast because tactics never stuck with me. What stuck were stories — business biographies, autobiographies, the decisions people made and why they made them. The principle only clicks once you know the story behind it.

So I built the thing I wanted to read. Every week I have two conversations with people who build in technology and product. Then I write the essay I wish I could find — one that puts you inside the conversation, through my eyes. What caught me off guard. What I kept thinking about after we hung up. Where the principle actually lives once you strip away the jargon.

I make this for myself first. If you read the way I do, you’ll want it too.

Subscribe to The Way of Product

PS — If you want to pitch coming on the show, or you know someone I should talk to, shoot me an email at [email protected] with "January752" in the subject line so it gets past my filters. I'm not optimizing for famous guests. I'm optimizing for interesting conversations, even from people who aren't LinkedIn influencers.

Listen to this episode on Spotify or Apple Podcasts

Adam Nash holds degrees in computer science with a focus on human-computer interaction, an MBA from Harvard, and has spent 25 years working at the overlap of engineering, design, and finance. His read: the best product work isn’t about solving rational problems — it’s about designing around the ways humans reliably behave irrationally. He built that argument across eBay, LinkedIn, Wealthfront, and now Daffy — where every feature exists to close the gap between what people want to do and what they actually do.

About forty minutes into our conversation, Adam Nash confesses something I believe to be the crux of our conversation.

“The anxiety I have alone — still, I don’t know how it is — I am almost 50 years old,” he says, “my anxiety in a hotel room of accidentally moving one of those items in the minibar and being charged for it is not rational. But it’s somehow very deep-seated.”

I almost laugh. Not at him — with him. Because Adam Nash is, by any reasonable measure, the person you’d least expect to be intimidated by a hotel minibar. He teaches a Stanford class called Personal Finance for Engineers. He ran Wealthfront. He was VP of Product at LinkedIn through the IPO. He was CEO of a fintech company that managed billions of dollars on behalf of its customers. If anyone on Earth should be able to glance at a $9 Toblerone and shrug, it’s him.

Instead, he tells me he gets nervous about it. And the way he tells me is what I keep thinking about. He doesn’t dress it up. He doesn’t make it a metaphor first and a confession second. He says it the way you’d admit to a friend at a bar that you still get butterflies before flying. The point isn’t that minibar anxiety is interesting. The point is that even Adam — the guy who has designed financial products for two decades — still has it. And that’s the entire thesis of his career.

We’ve been talking about Daffy, the company he founded in 2020. Daffy stands for the Donor Advised Fund For You, and it’s exactly what it sounds like: a tax-advantaged account for charitable giving. You put money in. It invests tax-free. Whenever you’re inspired to give, you go in, pick a charity, send the money. The wealthy have had access to this product for decades — Fidelity, Schwab, and Vanguard all offer one — but most people have never heard of it.

That gap, Adam tells me, is the entire opportunity. And the gap exists not because the math is hard, but because of something much stranger: people are not rational about money. Especially their own.

“Money is very rational,” he tells me. “Dollars and cents, right? You know, the math adds up. Like it’s either a good return or a bad return.” He says it the way someone reads aloud from a textbook they don’t fully agree with. Then he pivots. “But in the end, what’s the money for gets back to people — and people have feelings about money. They have feelings about what they’re doing with it, how they earned it, how they spend it, et cetera.”

This is the move that runs through everything he’s built. He stages the rational view first — the one MBAs are trained on, the one accountants live inside — and then he pulls it apart. Not because the rational view is wrong. Because it’s incomplete in the only way that matters: it doesn’t account for the actual humans who use the product.

I ask him how that lens — the irrationality lens — got into his work. He goes back to the early days of his career, when design was treated, in his words, “as almost an accessory marketing function — like make it pretty. Um, oh, make sure the brand is correct, the colors and text.” He’s not bitter about it, but you can hear something in the cadence — a person who watched an entire discipline be miscategorized for years and decided, at some point, to fix it where he could.

When he got to LinkedIn, he sat down with Reid Hoffman and made an argument that the company needed a horizontal design team — a team whose responsibility was the end-to-end experience, not any single page or feature. He’d spent his eBay years watching Web 1.0 products turn into “a library of pages and not really a product, not really an experience.” He didn’t want LinkedIn to become that. The team he built is still there.

But the more interesting story, to me, is what happened earlier. The career detail he drops almost as a footnote.

“I actually started,” he says, “I thought I was interviewing at a company called NeXT, but it turns out Apple acquired them in the middle. So I was there when Steve came back.”

He says this the way some people mention their college roommate. He worked on Rhapsody, which became Mac OS 10, which became the operating system most of us spent the next two decades using. He was there for the moment when Steve Jobs walked back into Apple and the entire trajectory of consumer computing changed. He’s not bragging. He’s setting up a different point. The Apple culture he watched — and later the Pixar culture he studied through Ed Catmull’s Creativity, Inc. — taught him that great products are made when designers, engineers, and operators don’t fight each other for primacy. They take each other’s instincts seriously.

“If they came up with an idea, there must be a good reason for it,” he says, paraphrasing the Pixar engineering team’s posture toward design. “Let’s figure out how to make that real and make that as excellent as possible.” And vice versa. It’s the win-win posture, he says, that makes a team transcend its parts.

I’ve worked at companies where this happens and at companies where it doesn’t, and the difference is night and day. He doesn’t romanticize it. He’s quick to point out the failure mode. “There’s a hubris that can set in with different roles,” he tells me, “where people decide that — no, engineering is the most important role, we can’t do this without it. Design is the most important role. And of course, product folks — product is the most important role.” He pauses, like he’s actually testing the claim against his own memory. “I think it misses the big picture.”

The big picture, in Adam’s telling, is that no one function ever shipped anything beautiful by itself. Beautiful products require people who can hold multiple frames at once. And the highest-value frame, in his career, has been the one that takes irrationality seriously.

I want to know how that frame translated into Daffy. So I ask him about a feature I noticed — the auto-deposit. You can have money debited from your account every week, every month, into your Daffy fund, before you ever decide where to give it. To me, that’s the whole product. You’ve already mentally separated the money from your life. By the time you sit down to give, the friction is gone.

He nods. This is the move he’s most proud of, I can tell, because his whole tone shifts. He starts using the word “we” more — the team voice. And he starts walking me through what he calls the most important insight of the company.

“Giving involves not one, but two hard problems for most people,” he says. “One is how much can I afford to give? And two, who do I give the money to? And the worst thing about the transactional system that we currently have is that you get hit with both of those problems.”

I have to stop and write this down, because it’s the cleanest articulation of a pattern I’ve watched fail thousands of people in dozens of contexts. Every donation page on the internet asks you both questions at the same time. Pick a charity. Pick an amount. Right now. Most people stall on one or the other and end up doing nothing. Or they default to the easiest option — give five dollars to a friend’s GoFundMe — and feel vaguely guilty that this isn’t what they meant by “I want to be generous.”

Adam tells me about the customer research he did before founding Daffy. He went around the country, talking to people about their giving. The thing that struck him wasn’t the diversity of opinions — though there was plenty of that. It was the consistency of one specific moment.

“You ask them how much they think they should give to charity every year — most people have an idea of what that is. But you ask them, did they hit their goal? And they all end up with this pregnant pause of no, you know, life got in the way, it got busy.”

The pregnant pause. He says it like he heard it dozens of times and stopped being able to un-hear it. Everyone had a number. Almost no one hit it. And the gap between intention and action — what he calls the Generosity Gap — wasn’t a values problem. It was a design problem.

This is the moment in our conversation when I realize what he’s actually doing at Daffy. He’s not trying to convince anyone to give more. He’s trying to remove the design friction that keeps generous people from acting on their own intent. The same way a 401(k) doesn’t make you save more — it just removes the moment of decision that you would otherwise fail at.

“It turns out with money, with finance, automating these things gets you to your goal more reliably and faster,” he says. “If we can do this with saving and investing, why can’t we do this with giving?”

He keeps coming back to this. The rational thing — the thing the textbook would say — is that adults should be able to set a giving goal and meet it through willpower. But adults can’t. And not because they’re bad. Because the system is built against them.

We get into the part of his thinking that he wrote about more than a decade ago, in an essay he called Finding the Heat. He tells me about being in marketing meetings where everyone wanted to talk about the brand’s positive attributes — hope, security, control. He’d push back. “We look at half the problem,” he tells me. “The world isn’t just filled with positive feelings.”

The negative emotions matter just as much. Maybe more. Fear. Anxiety. Embarrassment. The fear of messing something up. The fear of being charged for the minibar item you didn’t actually take. He’s not being cute when he tells me this — he’s giving me the same example he probably gave a marketing team a decade ago. Money has heat. If you design as if it doesn’t, you’re missing the actual problem.

And this is the place where his framework finally lands for me. Designers, when they’re doing the job at full strength, are behavioral economists. They’re not arranging pixels. They’re studying the predictable ways humans fail to do what they say they want to do — and then designing around it. The button isn’t bigger because bigger is prettier. The button is bigger because there’s a moment of doubt that you have to walk the user through. The default is opt-in because the literature on defaults is conclusive. The deposit happens before the decision because the research on pre-commitment is overwhelming.

Adam doesn’t say it this way. He doesn’t have to. The whole conversation is the proof.

I bring up Rory Sutherland — the Ogilvy executive who’s spent his career arguing that most things fail because we apply rational solutions to emotional problems. Adam smiles at the framing. He partly agrees. But he wants to add a wrinkle.

“I don’t know if you’ve met rational humans,” he says. “Please let me know. I know there are 8 billion on the planet. I’ve not met all.” He’s joking, but the joke has teeth. The framing of “rational vs. emotional” is itself a category error. There aren’t two camps. There’s one camp — humans — and they all have feelings about money, even when they’re trying not to. Even Adam. Even in a hotel.

We talk about Daffy Campaigns, the feature that lets members fundraise for causes and offer matching donations. He tells me about a member whose parent — a teacher — had passed away two decades earlier. On the anniversary of the death, the member ran a campaign to raise money for students. “That kind of story is not going to come out of a marketing team,” Adam tells me. “That kind of story is not going to come out of a corporate kind of process. These are personal stories that people are telling.”

He says it quietly. We’ve been talking for over an hour and the energy in his voice has settled into something I’d call admiration — for the people using the product more than for the product itself. He keeps saying “we” when he talks about Daffy, but when he talks about the campaigns, he says “they.” The members. The givers. The teacher’s child. The company is the scaffolding. The campaigns are the building.

I ask him to wrap things up however he wants. He doesn’t pitch. He doesn’t ask anyone to download anything. He says one thing that I’ll keep returning to.

“It really does feel good,” he tells me, “to realize that some of the benefit of your skill, of your work, of your life, could benefit others.”

Then, almost as an afterthought, he tells me what people say after they’ve used the product for a while. It’s not that they saved money on taxes. It’s not that the interface was nice. It’s that the product made them feel good about how they were teaching the next generation.

Which is, I realize after we sign off, the most behavioral-economics thing he could have told me. The product’s measurable outputs — dollars donated, accounts opened, charities funded — are not what closes the loop with the user. The feeling does. The story they tell themselves about who they are when they use it. The image of their kid asking what the donation is for, and them having an answer.

That’s the gap that was always there. Adam built a product that closed it. And the only reason he could see the gap in the first place is that he never bought the premise that designers are decorators. He understood, going back to NeXT and Steve Jobs and Reid Hoffman and the Generosity Gap, that designing for humans is the same job as designing around their irrationality.

Giving isn’t a values problem, it’s a tooling problem.When it’s a tooling problem — it’s a design problem.

About Adam Nash

Adam Nash is the Co-Founder and CEO of Daffy, a donor-advised fund platform revolutionizing charitable giving. Rising to prominence in the 2010s as a product leader across Silicon Valley’s most influential technology companies, Nash became known for scaling platforms to hundreds of millions of users and pioneering new categories in fintech.

Previously, as Vice President of Product & Growth at Dropbox from 2018 to 2020, Nash led the teams responsible for growth, product strategy, product management, and product analytics for a platform serving over 600 million registered users with responsibility for approximately 90% of all company revenue in 2019. Before Dropbox, he served as President and CEO of Wealthfront from January 2014 to October 2016, where he championed the creation of automated investment services and grew the company’s client base by over 60x while scaling assets under management 45x from less than $100 million to over $4 billion.

His career highlights include serving as Vice President of Product Management at LinkedIn, where he led the company’s Platform & Mobile products including the launch of LinkedIn’s open developer platform and native applications. Nash founded LinkedIn Hackdays, a program instrumental in driving the company’s innovation culture, and led search, cloud efforts, and user experience design teams. Earlier in his career, he held strategic and technical roles at eBay and Apple.

As an angel investor since 2011, Nash has invested in over 150 seed-stage companies including Figma, Gusto, Opendoor, Firebase (acquired by Google), and Boom Supersonic. He has served as an Adjunct Lecturer at Stanford University since 2017, teaching CS 007: “Personal Finance for Engineers.” Nash holds BS and MS degrees in Computer Science from Stanford University and an MBA from Harvard Business School.

Hey, Thanks for reading this. I mean that. There's a lot of content out there competing for your attention, and you spent some of it here. I hope it was worth it. Even better, I hope it prompted you to think about something differently enough that you'd share it with someone who'd get something out of it too.I started this podcast because tactics never stuck with me. What stuck were stories — business biographies, autobiographies, the decisions people made and why they made them. The principle only clicks once you know the story behind it.

So I built the thing I wanted to read. Every week I have two conversations with people who build in technology and product. Then I write the essay I wish I could find — one that puts you inside the conversation, through my eyes. What caught me off guard. What I kept thinking about after we hung up. Where the principle actually lives once you strip away the jargon.

I make this for myself first. If you read the way I do, you’ll want it too.

Subscribe to The Way of Product

PS — If you want to pitch coming on the show, or you know someone I should talk to, shoot me an email at [email protected] with "January752" in the subject line so it gets past my filters. I'm not optimizing for famous guests. I'm optimizing for interesting conversations, even from people who aren't LinkedIn influencers.

About Pete Hunt

Pete Hunt is the Chief Executive Officer at Dagster Labs, the company behind the open‑source data orchestration platform Dagster and its commercial Dagster Cloud offering. Rising to prominence in the early 2010s, he became known as one of the early leaders of the React.js project inside Facebook and as a key engineering voice at Instagram during its hyper‑growth period. Today he is widely regarded as an influential figure at the intersection of data platforms, infrastructure, and developer experience, helping teams modernize how they build and operate data‑intensive systems.

Previously, as Head of Engineering and then CEO at Dagster Labs, Hunt helped guide the organization from its early identity as Elementl, founded in 2019, to a commercial data orchestration leader with the launch of Dagster Cloud and the introduction of Software‑Defined Assets in 2021. After joining the company in early 2022, he assumed the CEO role in November 2022 and has since focused on turning Dagster’s open‑source traction into a scalable business with a repeatable go‑to‑market motion. Under his leadership, Dagster Labs has grown into a well‑funded, small but highly specialized team shipping infrastructure that supports thousands of data assets across modern data stacks.

His career highlights include a formative stretch at Facebook beginning around 2011, where he was a founding member of the React.js team and helped drive its transformation from an internal experiment into one of the most widely adopted front‑end frameworks in the world. After the Instagram acquisition in 2012, Hunt became the first Facebook engineer embedded into Instagram, led the instagram.com web team, and built Instagram’s business analytics products as the company scaled to hundreds of millions of users. In 2014 he co‑founded abuse‑fighting startup Smyte, serving as CEO for roughly four years until its acquisition by Twitter in 2018, where he then worked on Trust & Safety and infrastructure during a period when the platform handled hundreds of millions of daily active users. Across these roles he has consistently operated at the point where new infrastructure—React, Instagram’s web stack, Smyte’s anti‑abuse systems, and now Dagster—becomes robust enough to support global‑scale products.

Outside his operating roles, Hunt has built a durable reputation as a conference speaker and educator, giving talks at events such as OSCON 2014 on how instagram.com works and sharing practical lessons on React, data platforms, and engineering leadership. Through long‑form interviews and podcasts, he documents the transition from individual engineer to founder and CEO, making him a widely referenced voice for engineers moving into executive roles.

Hey—Thanks for reading this. I mean that. There's a lot of content out there competing for your attention, and you spent some of it here. I hope it was worth it. Even better, I hope it prompted you to think about something differently enough that you'd share it with someone who'd get something out of it too.I started this podcast because tactics never stuck with me. What stuck were stories — business biographies, autobiographies, the decisions people made and why they made them. The principle only clicks once you know the story behind it.

So I built the thing I wanted to read. Every week I have two conversations with people who build in technology and product. Then I write the essay I wish I could find — one that puts you inside the conversation, through my eyes. What caught me off guard. What I kept thinking about after we hung up. Where the principle actually lives once you strip away the jargon.

I make this for myself first. If you read the way I do, you’ll want it too.

Subscribe to The Way of Product

PS — If you want to pitch coming on the show, or you know someone I should talk to, shoot me an email at [email protected] with "January752" in the subject line so it gets past my filters. I'm not optimizing for famous guests. I'm optimizing for interesting conversations, even from people who aren't LinkedIn influencers.

Listen to this episode on Spotify or Apple Podcasts

Yaron Schneider is the Founder and Chief Technology Officer at Diagrid, where he leads the development of distributed systems platforms that power durable workflows and AI agents for cloud-native teams worldwide. Rising to prominence in the late 2010s through his work on cloud infrastructure at Microsoft, he became known for co-creating the CNCF projects Dapr and KEDA, which today serve tens of thousands of organizations building microservices and event-driven applications. As Chair of the Workflows Working Group at the Agentic AI Foundation, he is widely regarded as an influential figure in defining how large-scale agentic systems are orchestrated and operated in production.

Previously, as Principal Software Engineer on Azure Container Apps at Microsoft, Schneider helped design and ship a serverless platform that enabled customers to run containerized microservices and event-driven workloads without managing Kubernetes directly, driving adoption across thousands of production clusters and multi-million-dollar cloud accounts. In earlier roles on the Azure CTO Incubations team, he focused on high-scale distributed systems and developer platforms, work that culminated in Dapr’s acceptance into the Cloud Native Computing Foundation in 2021 and its graduation to top-tier status in 2024, alongside Kubernetes and Prometheus. By 2025, the Dapr ecosystem was engaging over 40,000 companies across finance, healthcare, retail, and SaaS, and more than 90% of surveyed developers reported measurable time savings when building distributed applications with the runtime.

Schneider’s career highlights also include serving as Division CTO at ironSource from 2013 to 2015, where he led engineering for high-throughput advertising and monetization systems processing billions of events per day across mobile and desktop. Earlier, as a software architect at SuperDerivatives and a hands-on architect at Ness Technologies, he worked on low-latency, mission-critical platforms in financial technology and enterprise software, gaining the deep distributed-systems experience that later shaped his open-source work. Through Dapr, KEDA, and Diagrid’s Catalyst platform, Schneider’s contributions have helped standardize patterns such as workflow-as-code, event-driven autoscaling to and from zero, and durable agentic workflows across Kubernetes and multi-cloud environments.

Hey, Thanks for reading this. I mean that. There's a lot of content out there competing for your attention, and you spent some of it here. I hope it was worth it. Even better, I hope it prompted you to think about something differently enough that you'd share it with someone who'd get something out of it too.I started this podcast because tactics never stuck with me. What stuck were stories — business biographies, autobiographies, the decisions people made and why they made them. The principle only clicks once you know the story behind it.

So I built the thing I wanted to read. Every week I have two conversations with people who build in technology and product. Then I write the essay I wish I could find — one that puts you inside the conversation, through my eyes. What caught me off guard. What I kept thinking about after we hung up. Where the principle actually lives once you strip away the jargon.

I make this for myself first. If you read the way I do, you’ll want it too.

Subscribe to The Way of Product

PS — If you want to pitch coming on the show, or you know someone I should talk to, shoot me an email at [email protected] with "January752" in the subject line so it gets past my filters. I'm not optimizing for famous guests. I'm optimizing for interesting conversations, even from people who aren't LinkedIn influencers.

Benjamin Johnson is the Founder and CEO at Particle41, where he leads a global software consultancy that has operated for more than 12 years across remote teams in the Dallas–Fort Worth metroplex and beyond. Rising to prominence in the 2010s, he became known for building high-performing engineering organizations that ship end-to-end digital products, from cloud-native platforms to AI-ready application modernization. As a fractional CTO and podcast host, he is widely regarded as an influential figure for founders seeking to scale technology capabilities without sacrificing speed or reliability.

Previously, as Chief Technology Officer at DOCKWORKS INC, he architected a web-based marine management platform that grew to serve more than 100 marine businesses in roughly 2 years before being acquired by DockMaster in 2023. In this role he led work order management, vessel tracking, and billing capabilities that helped streamline operations for small marine shops and boatyards while overseeing a full product and engineering organization. He also guided the post-acquisition integration, ensuring continuity for customers and enabling a combined product roadmap across two brands.

His career highlights include serving as Director of Software Engineering at LegalZoom, where he revamped the company’s Robotic Process Automation strategy, created excellence in document automation, and developed a company name-check algorithm that achieved approximately 98% state acceptance prediction accuracy for new business names. Earlier, as Co-Founder and CTO of Legalinc Corporate Services Inc., he helped grow the enterprise legal automation platform from zero to a successful exit to LegalZoom in about three years, building a one-of-a-kind legal filing API that secured partnerships with platforms such as Stripe Atlas, Yahoo Small Business, and Amazon. At IntelliCentrics, he managed DevOps for roughly 125 servers across three data centers, implemented auto-scaling and continuous delivery, and upheld a 99.9% uptime promise while training teams to independently extend automation.

As host of the Particle Accelerator podcast, he interviews technology and business leaders on strategic problem-solving, digital transformation, and leadership at scale. Through this work and frequent guest appearances on other shows, he continues to shape how founders, CEOs, and engineering leaders think about modern software development, DevOps, and AI-enabled growth.

Hey, Thanks for reading this. I mean that. There's a lot of content out there competing for your attention, and you spent some of it here. I hope it was worth it. Even better, I hope it prompted you to think about something differently enough that you'd share it with someone who'd get something out of it too.I started this podcast because tactics never stuck with me. What stuck were stories — business biographies, autobiographies, the decisions people made and why they made them. The principle only clicks once you know the story behind it.

So I built the thing I wanted to read. Every week I have two conversations with people who build in technology and product. Then I write the essay I wish I could find — one that puts you inside the conversation, through my eyes. What caught me off guard. What I kept thinking about after we hung up. Where the principle actually lives once you strip away the jargon.

I make this for myself first. If you read the way I do, you’ll want it too.

Subscribe to The Way of Product

PS — If you want to pitch coming on the show, or you know someone I should talk to, shoot me an email at [email protected] with "January752" in the subject line so it gets past my filters. I'm not optimizing for famous guests. I'm optimizing for interesting conversations, even from people who aren't LinkedIn influencers.

About my guest & how to find them online

Karl Simon is the Co-Founder and CTO of Subatomic AI, an enterprise AI Co-Worker Agent platform that deploys customizable agents adapted to client workflows, philosophies, and reasoning patterns. Rising to prominence in the 2010s as a data and engineering leader across retail, healthcare, and life sciences, Simon became known for building globally distributed data organizations and modernizing legacy platforms to support AI and machine learning at scale. Subatomic, co-founded with CEO Sam Sova and backed by a $7 million seed round in October 2025 led by Vantage Financial, focuses on high-stakes verticals including wealth management, legal, and manufacturing.

Previously, as a senior technology leader at Hudson’s Bay Company — the retail conglomerate that housed Saks Fifth Avenue, Lord & Taylor, Gilt.com, and other brands now consolidated under Saks Global — Simon led all engineering, business intelligence, and AI/ML functions across the company. Before that, he served in data engineering and analytics leadership roles at Komodo Health, Accenture, and Genentech, building AI-enabled decisioning platforms and modernizing source-to-target data pipelines across healthcare and life sciences.

Earlier in his career, Simon joined Oracle in manufacturing distribution, where he self-taught data warehousing from Ralph Kimball’s Data Warehouse Toolkit before applying those techniques to improve same-day order fulfillment insights. That formative experience established his approach to grounding AI systems in well-architected data foundations — a philosophy he has carried through more than three decades of digital transformations spanning mobile, big data, and generative AI.

Hey, Thanks for reading this. I mean that. There's a lot of content out there competing for your attention, and you spent some of it here. I hope it was worth it. Even better, I hope it prompted you to think about something differently enough that you'd share it with someone who'd get something out of it too.I started this podcast because tactics never stuck with me. What stuck were stories — business biographies, autobiographies, the decisions people made and why they made them. The principle only clicks once you know the story behind it.

So I built the thing I wanted to read. Every week I have two conversations with people who build in technology and product. Then I write the essay I wish I could find — one that puts you inside the conversation, through my eyes. What caught me off guard. What I kept thinking about after we hung up. Where the principle actually lives once you strip away the jargon.

I make this for myself first. If you read the way I do, you’ll want it too.

Subscribe to The Way of Product

PS — If you want to collaborate on the show, or you know someone I should talk to, shoot me an email at [email protected] with "January752" in the subject line so it gets past my filters. I'm not optimizing for famous guests. I'm optimizing for interesting conversations, even from people who aren't LinkedIn influencers.

Listen to this episode on Spotify or Apple Podcasts

“No one could ever take this over,” Jake says. “If he left or somebody else had to manage it, no one knows what’s going on here.”

Jake Stauch tells me a story about a CFO and an expense report, and it changes the way I think about no-code tools.

The CFO had a simple request for his IT team: when someone submits an expense report, get approval from an M5 manager or above, go up the chain, but if you reach the CEO you have gone too far -- drop down to the closest manager for review. One sentence. Clean logic. Makes perfect sense.

Then the IT leader pulled up Okta workflows to show Jake what he had built. “He has to scroll and scroll and scroll,” Jake tells me, “because there are hundreds of nodes and connectors and if-this-then-that and error handling.” Two months of work for a one-sentence business rule. The IT leader was proud of it. He should have been. It was technically impressive. But Jake saw something else entirely.

This is the dirty secret of every no-code workflow builder. They are supposed to make automation accessible to non-technical people, but the moment the logic gets even slightly complex, you end up with a sprawling visual spaghetti that is too technical for the business users who wanted a simple solution and too constrained for the engineers who could have written the code in a fraction of the time.

“It’s too technical for non-technical users,” Jake says. “It’s not technical enough for the folks that really want to get in the weeds.”

The worst of both worlds. I feel this in my bones. I spent two grand hiring someone to teach me how to wire together Airtable and Zapier for podcast production. The planning phase was the hardest part. You have to know what the tools can do before you can design the automation, and once you build it you are managing 20 interconnected flow charts that will break in ways nobody can debug.

I ask Jake what Serval does differently, and his answer is architectural, not cosmetic.

“Everyone who’s ever approached automation has started with this idea of a drag-and-drop workflow builder,” he says. “Every generation of these systems has basically said, okay, we’re gonna build a better workflow builder. They make the UI better, they make it easier to configure, but they fundamentally don’t change the structure.”

Serval changed the structure. Their insight: if AI can write code from a natural language description, then the code is the source of truth, not the blocks. And if the code is the source of truth, the visual layer -- the flowchart the user sees -- does not need to map one-to-one to the underlying logic.

“The block is not real,” Jake says. “It’s just a visual representation of what the code’s doing.”

I stop him. This is the line I keep coming back to. Every no-code tool in history has assumed that the visual representation is the logic. The blocks are not just a display layer -- they are the actual mechanism. Move a block, and you change the code. Add a connector, and you create a dependency. The visual and the logical are fused. That fusion is what creates the spaghetti.

Serval severed it. The AI writes concise, efficient code that handles all the branching, looping, null checks, and error handling that would stretch into hundreds of visual nodes in a legacy tool. Then Serval generates a clean visual summary that makes the workflow easy to follow -- but the visualization is an abstraction, not the system itself.

This is the equivalent of what happened with iOS design. I bring up the Jony Ive story -- how early iOS used skeuomorphic metaphors like green felt and Rolodexes to teach people what a touchscreen could do. Once users understood the paradigm, Ive stripped the metaphors away. The training wheels came off.

Jake’s customers went through the same transition. In the early days, they would see Serval’s interface and reach for what they knew. “You could see they almost missed the old way of doing it,” Jake says. “They’re like, well, what if I wanna click into that block and change the configurations?” And Jake had to say: there is no block. Just chat with the system. Tell it what you want changed.

“I think in the early days, that was an unfamiliar user action,” he tells me. But consumer AI moved the culture. Enterprise buyers go home and use ChatGPT. The expectation of a chat-based interaction went from unfamiliar to obvious in about a year.

The compression of build cycles is staggering. What used to take weeks or months now happens in a conversation. An IT team member describes an onboarding workflow. Serval writes the code. Generates a visual representation. The whole thing is live. If the business process changes -- and it will -- you just tell the system what to change. No scrolling through hundreds of nodes trying to find the right branch to modify.

I think about this in the context of Mike Tyson’s line: everyone has a plan until they get punched in the mouth. Every legacy automation is one business process change away from obsolescence. The two-month Okta workflow is already out of date by the time it ships because the CFO changed the approval threshold. With Serval, the CFO changes the requirement and someone on the IT team tells the system in plain language. Done.

Jake tells me that the really cool part is what happens next. The IT teams that start building with Serval become evangelists. HR wants in. Finance wants in. Legal wants in. IT transforms from ticket processors into what Jake calls an automation center of excellence.

“The block is not real” is not just a technical insight. It is a liberation. Twenty years of workflow tools built on the wrong assumption, and Jake Stauch had the nerve to throw it out.

About Jake Stauch

Jake Stauch is the Co-Founder and CEO of Serval, an AI-native platform that automates enterprise employee support through natural language-to-code workflow generation. Rising to prominence in the mid-2010s as a founder and product executive at the intersection of hardware and enterprise software, Stauch became known for identifying friction bottlenecks in IT automation and building infrastructure-first AI systems before the underlying technology fully matured. Serval, co-founded in April 2024 alongside CTO Alex McLeod, reached a billion-dollar valuation within 18 months of founding after raising $125 million across three rounds led by General Catalyst, Redpoint Ventures ($47M Series A), and Sequoia ($75M Series B).

Previously, as Director of Product at Verkada from 2019 to 2024, Stauch spent five years conducting customer discovery with enterprise IT departments across physical security hardware and software. There, he identified the automation paradox that would become Serval’s founding insight: despite a growing landscape of automation tools, most IT requests were still handled manually because the friction of building workflows exceeded the cost of doing the tasks by hand. His product work at Verkada spanned new product lines in physical security cameras, access control systems, and alarm hardware sold to Fortune 500 IT departments.

Earlier, Stauch founded NeuroPlus, a brain-sensing hardware and cognitive performance software company, which he led as CEO from 2012 to 2019. He was recognized on the Forbes 30 Under 30 list in 2017 for this work, which included a patent for an EEG-based neurofeedback system. He holds a degree from Duke University.

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Radhika Dutt is the author of Radical Product Thinking, a product leadership movement and book that has been translated into multiple languages, including Chinese and Japanese. Rising to prominence in the 2010s and 2020s, she became known for codifying a vision-driven alternative to iteration-led product development used by teams across industries from fintech to government. She currently serves as Advisor on Product Thinking to the Monetary Authority of Singapore (MAS), where she helps steer digital transformation and user-centric product delivery at one of Asia’s most influential financial regulators.

Previously, as Author and Speaker at Radical Product Thinking starting in 2017, Dutt built a global practice around a five-part methodology spanning vision, strategy, prioritization, execution and measurement, and culture. Her work equips organizations to diagnose and cure “product diseases” such as feature bloat and metric-driven drift, enabling leaders to align teams around a clear, shared change they seek to bring about in the world. Through keynotes at conferences like Productized and client work with startups and large enterprises, she has trained thousands of product practitioners and executives on how to translate vision into a repeatable operating system for innovation.

Her career highlights include founding two companies that were successfully acquired, contributing to a total of five acquisitions across broadcast, media and entertainment, telecom, advertising technology, and robotics over more than 20 years in product. As an MIT-trained engineer with an S.B. and M.Eng. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (1995–2000), she has applied product thinking to domains as varied as consumer apps, government services, and even wine, demonstrating the portability of her framework across sectors measured in billions of dollars of market value. She is widely regarded as an influential figure in the product management community for shifting organizations away from purely metric- and OKR-driven roadmaps toward what she calls “vision-driven transformation.”

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Discover the root cause analysis methods and narrative-driven measurement that prevent feature factories while maintaining innovation velocity.

“It’s b******t statements, right, that are slim on the details.”

Radhika Dutt doesn’t hedge when describing most product visions. Twenty-five years after founding her first startup at MIT with a vision to “revolutionize wireless,” she can admit what most product leaders won’t: she has no idea what that meant. The company had five co-founders, dorm room origins, and all the trappings of a Silicon Valley success story. What it didn’t have was clarity about the problem they were solving.

“I don’t even know what we meant by that,” she says, and something shifts in her tone. The polished product consultant gives way to someone examining an old wound. “But it was this idea of just being big, scaling. Now, you know, even today when you look at so many Silicon Valley startups, that’s sort of the mistake you often see, right?”

She calls these mistakes product diseases. Not problems or challenges—diseases. The language is deliberate. Diseases are things you catch without realizing it, things that spread through organizations, things that require diagnosis and systematic treatment rather than quick fixes.

The disease at that first startup was hero syndrome: the obsession with scale and growth without understanding what problem needs solving. But Radhika discovered something worse during her subsequent career across five acquisitions. Most product teams suffer from multiple diseases simultaneously, creating what she now recognizes as an epidemic of confused priorities and wasted effort.

“And I call them product diseases because it’s just so ubiquitous and we need to talk openly about these product diseases. ‘Cause you know, it’s just so easy to catch.”

The solution she developed—radical product thinking—starts with a fill-in-the-blanks approach to vision setting that forces teams to confront what they’re actually trying to accomplish. Not the aspirational version, not the pitch deck version, but the detailed, actionable version that can guide daily decisions.

“So today, when amateur wine drinkers want to find wines that they’re likely to like and to learn about wine along the way, they have to find attractive looking wine labels or find wines that are on sale. This is unacceptable because it leads to so many disappointments and it’s really hard to learn about wine in this way. We are bringing about a world where finding wines you like is as easy as finding movies you like on Netflix. We are bringing about this world through a recommendations algorithm that matches wines to your taste and an operational setup that delivers these wines to your door.”

She pauses after reciting this vision for her wine startup, which she founded in 2011 and sold in 2014. “Now this is a radical vision because I hadn’t told you anything about my startup, and yet hopefully when I shared this vision, you knew exactly what we were doing and why we were doing it.”

The contrast with “revolutionize wireless” is stark. One vision contains a specific customer segment, their current painful experience, why that experience is unacceptable, the desired future state, and the concrete mechanism for achieving it. The other contains marketing language that could apply to any telecommunications company.

But even teams that develop clear visions struggle with what Radhika calls the second product disease: hyperemia. The obsession with moving numbers up and to the right, regardless of whether those numbers drive long-term value.

“You know, the moment I say this, people are usually like, oh yeah, I get it. We have it. Hyperemia is this obsession with moving numbers up and to the right. Having all sorts of wonderful dashboards that all look green. But those are not even necessarily the right metrics. And sometimes they may even be the right metrics, but they drive you in the wrong direction.”

The dating app industry provides her favorite example of hyperemia in action. When Tinder launched swipe left/swipe right in 2013, user engagement metrics exploded. Every other dating app copied the mechanic because the numbers looked incredible. User engagement up, time on app up, all the key performance indicators trending toward success.

“So, you know, everyone was thrilled with these metrics, but what was happening if you looked at the longer term effect? The more they gamified intimacy, it was creating a toxic dating environment, the more it was dehumanizing interactions. And so what it created in the long term was user fatigue.”

The result: dating app backlash, mass user deletions, and in 2025, Bumble laying off 30% of its staff. The entire industry fell into a slump because short-term metric optimization destroyed the long-term value proposition. The numbers looked great right up until they didn’t.

“So my point is, hyperemia is one of these diseases where you can do fantastic and making numbers look great. And genuinely they may be the right numbers, but that’s not necessarily good for your product or good for your business in the long term.”

This is where most conversations about metrics and OKRs devolve into tactical debates about choosing better numbers or preventing gaming. Radhika thinks those discussions miss the fundamental issue: goals and targets create perverse incentives regardless of how carefully they’re designed.

“Even when someone doesn’t have malicious intent and they’re not trying to game metrics, the subconscious incentive you have is to show you’re a high performer and therefore focus on the numbers that look good, that show OKRs to be green, as opposed to focus on numbers that, you know, OKRs aren’t even measuring, but that are indicating a problem and that say, hey, there’s something off here.”

She illustrates with her experience at Avid, the company behind video editing software used for every Oscar-winning film in Hollywood. The numbers looked fantastic—sales targets consistently hit or exceeded. But underneath the green dashboards, a different story was unfolding.

“If you just looked under the hood, you would see a different scenario. The way we were hitting our sales targets was by moving further and further into the high end because our low end was being eroded by Apple and Adobe.”

The company was achieving its goals by retreating upmarket as competitors commoditized the lower tiers. The sales numbers stayed strong, but the strategic position was deteriorating. Instead of asking why the low end was being eroded or how Apple and Adobe’s business models differed, leadership focused on maintaining the metrics that made them look successful.

“The incentive is I wanna show that I’ve hit those goals and targets things are working. I wanna prove that our, that things are going well.”

This dynamic—prioritizing the appearance of success over understanding reality—is what legendary Intel CEO Andy Grove meant when he said leaders are the last to know. When you set goals and targets, everyone wants to tell you the good news. Bad news gets buried because it threatens the narrative of progress.

The alternative Radhika proposes isn’t better goal-setting. It’s puzzle-setting. Instead of declaring what numbers teams should hit, leaders should define what problems need solving and create frameworks for teams to investigate those problems systematically.

“So what I am working on in this next book. And what I advocate for is a mindset shift instead of goals and targets. It’s a mindset of puzzle setting and puzzle solving. And then the way you measure people is how well are they solving this puzzle? Are we making progress towards solving this puzzle?”

Her framework for puzzle-setting uses three O’s: Observation, Open Questions, and Objective. The observation captures what’s actually happening, not just what the metrics show. The open questions identify what the team doesn’t understand about the observation. The objective summarizes the puzzle that needs solving.

For Avid, the observation would have been: “Our low end is getting eroded by Apple and Adobe in the mid-tier. This is what’s happening. The market is getting eroded. The way we’re making the numbers is by going further into the high end.”

The open questions would probe deeper: “What is happening on the low end? Adobe and Apple are successful there. What is their business model? Can we fight this business model in a different way? Is there something we can offer that can be a complete workflow for the low end where even if Apple and Adobe are giving away the editor, people will want it and want to pay for it?”

The objective becomes: “Figure out what do we do in our video editing business. Do we invest in it, do we not, or how do we invest in it, so that we can continue to either be successful in the video editing business, or we choose to move on and adapt our business?”

This is puzzle-setting. It creates space for teams to investigate reality rather than optimize metrics. But puzzle-setting only works if teams have the skills and safety to solve puzzles effectively.

That’s where puzzle-solving comes in: three questions that teams answer as they work on the puzzle. How well did it work? What did we learn? What will we try next?

“Notice how this question, it’s not binary, did you or didn’t you hit this target? It’s not just putting you on the spot, making you feel like I have to prove something. It’s genuinely inviting the good and the bad. This is how as a leader, you’re not the last to know you’re inviting the good and the bad.”

The second question—what did we learn—requires narrative synthesis, not just dashboard reporting. Teams have to look at all their data and tell the story of what’s really happening with users, markets, and competitors.

The third question—what will we try next—forces strategic thinking based on actual learning rather than predetermined roadmaps.

“I can really tell based on working with a team who is thinking deeply and how well they’re solving the puzzle based on their answers to what have we learned and what will we try next? That’s how you can evaluate people, not just based on ta-da, I’ve hit my numbers.”

The transformation this creates in team dynamics is profound. Instead of competing to show green dashboards, team members compete to solve interesting problems. Instead of hiding bad news, they compete to surface the most important insights. Instead of gaming metrics, they compete to design better experiments.

But this approach requires a level of psychological safety that’s rare in most organizations. Teams have to be willing to admit what’s not working, leaders have to be willing to hear it, and everyone has to be willing to change direction based on what they learn.

“Did you know that he didn’t keep a corner office? He used to have a cubicle, same size cubicle as everyone else because he wanted everyone to challenge his ideas and to feel like they could speak up. Very few leaders want people to speak up and tell them this is not working.”

The Andy Grove reference isn’t accidental. Grove understood that organizational hierarchy creates information distortion. The further you are from the work, the more filtered your information becomes. Physical proximity—sharing the same kind of workspace as everyone else—was one way to counteract that distortion.

Most leaders won’t give up their corner offices. But they can start role-modeling the kind of reflection and transparency they want from their teams. Taking time in meetings to discuss what didn’t work in past initiatives. Sharing their own learning and uncertainty. Creating space for teams to investigate puzzles rather than just hit targets.

“You can role model for your team, the psychological safety and sharing the good and the bad of what didn’t work, what you learned from it, what you’re going to try next. You can role model this so that you can invite the team to solve puzzles like you are.”

For individual contributors stuck in goal-driven organizations, Radhika recommends starting small. Take a past feature release and work through the three puzzle-solving questions privately. Look at the data, but focus on the narrative: what really happened with users? What did the numbers mean in context? What would you try differently next time?

Once you’ve practiced this approach yourself, try it in one-on-ones with your manager or conversations with peers. Create small bubbles of psychological safety where honest reflection and learning can happen.

“Instead of just chasing OKRs, you’re working on puzzles. Puzzles are so much more fun. We are all energized by puzzles. Instead of just focusing on OKRs, think about what puzzles you’re solving for the company. That in itself will energize you for your work.”

The energy difference is real. Goals feel imposed—something you have to hit to prove your worth. Puzzles feel intrinsic—something you want to solve because the solution creates value. The shift from external validation to internal motivation changes how people approach their work.

But the business results matter too. Radhika’s recent consulting engagement provides a concrete example. A company stuck with stalled sales in 2023 doubled sales in 2024, then doubled again in 2025 after switching from goal-setting to puzzle-solving. Customer churn dropped from 26% to 4%.

“We did all of that by puzzle setting and puzzle solving instead of being driven by OKRs.”

The transformation didn’t happen overnight. It required leaders willing to let go of familiar frameworks, teams willing to embrace uncertainty, and everyone willing to prioritize learning over looking good.

The alternative—continuing with product diseases like hero syndrome and hyperemia—leads to the dating app outcome. Short-term metrics that mask long-term erosion. Features that optimize for engagement instead of value. Teams that hit their numbers while slowly destroying what they’re trying to build.

“Or are we all doomed to just constantly learning from these failures, making mistakes and having to learn the hard way?”

That was the question that drove Radhika to develop radical product thinking in the first place. After watching team after team catch the same diseases, make the same mistakes, and suffer the same consequences, she wanted to understand whether systematic approaches could prevent predictable problems.

The answer is yes, but only if teams are willing to diagnose their diseases honestly and treat them systematically. Most organizations prefer to treat symptoms—choosing better metrics, writing clearer requirements, running more experiments—rather than address root causes.

The root cause is the gap between great ideas and great products. Steve Jobs called it out in his lost interview: most people think the idea is 90% of the work when it’s actually 5%. The other 95% is the systematic translation of vision into strategy, strategy into priorities, and priorities into daily activities.

“And I think filling that gap is exactly what I talk about in terms of systematically translating a vision for change into action, into everyday activities. And that’s how we close that gap.”

Product diseases spread when teams try to shortcut that translation process. Hero syndrome emerges when teams skip from big vision to scaling without defining the problem. Hyperemia emerges when teams skip from activities to metrics without understanding the connection to long-term value.

The systematic approach isn’t glamorous. It requires detailed problem statements, clear frameworks, consistent reflection, and honest measurement. It requires admitting when things aren’t working and changing direction based on learning rather than predetermined plans.

But it’s the difference between revolutionary wireless and amateur wine drinkers who can’t find wines they like. One vision launches a company that doesn’t know what it’s doing. The other launches a company that gets acquired because it solves a real problem in a specific way.

“Now this is a radical vision because I hadn’t told you anything about my startup, and yet hopefully when I shared this vision, you knew exactly what we were doing and why we were doing it.”

That clarity—knowing exactly what you’re doing and why—is what prevents product diseases from taking hold. It’s what enables teams to choose long-term value over short-term metrics. It’s what transforms abstract strategies into concrete progress.

The vision template is just the beginning. The systematic framework for translating vision into action is what makes the vision matter. And the puzzle-solving approach is what keeps teams connected to reality as they execute against the vision.

Twenty-five years after revolutionizing wireless, Radhika has learned to revolutionize something more specific: how product teams think about the problems they’re trying to solve. Not with better tools or processes, but with better questions and frameworks for finding answers.

The questions aren’t complicated. What problem are we solving? Why does it need to be solved? How will we solve it? How well is our solution working? What are we learning? What will we try next?

The complexity comes from creating organizational conditions where teams can ask those questions honestly and act on the answers systematically. Where puzzle-solving is rewarded over performance theater. Where learning from failure is valued more than hitting arbitrary targets.

“Puzzles are so much more fun. We are all energized by puzzles.”

That energy—the intrinsic motivation to solve interesting problems—might be the strongest antidote to product diseases. When teams are genuinely curious about the puzzles they’re solving, they’re less likely to settle for b******t statements that are slim on details. They’re more likely to demand the clarity that prevents revolutionary wireless from becoming just another failed startup story.

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Saurabh Sharma is the Chief Product Officer at You.com, where he leads product, design, and research for AI agents that power critical business workflows across search and enterprise use cases. Rising to prominence in the 2010s through work at Google, he became known for scaling applied AI, search and discovery, and trust and safety systems to hundreds of millions of users globally. He is widely regarded as an influential figure at the intersection of AI assistants, consumer products, and infrastructure for large-scale machine learning.

Previously, as Head of Search Products at OpenSea, Saurabh led a multi-product portfolio spanning search, discovery, trust and safety, and core web and mobile platforms during the 2022–2023 NFT market cycle. He became known for steering product strategy in a period when OpenSea supported millions of users and billions of dollars in NFT trading volume annually, focusing on safe discovery and high-intent search in a volatile, web3-native marketplace. His leadership aligned search quality, fraud prevention, and creator-centric experiences in an ecosystem that operated 24/7 across global markets.

His career highlights include an 11-year tenure as a Group Product Manager at Google, where he led teams of more than 12 product managers and 100 engineers building AI-powered experiences in Google Assistant, Search, Maps integrations, identity, and monetization from 2011 to 2022. At Google, he helped ship and scale products such as Google Assistant’s AI search integrations, Family Link and Google Accounts for kids, Google+, and Gmail, each serving hundreds of millions of monthly active users and operating across more than 100 countries. Earlier, as an Advisory Software Engineer at IBM from 2005 to 2010, he developed core AIX UNIX kernel infrastructure for virtual memory, including Active Memory Expansion and Large Segment Aliasing, contributing to enterprise systems that powered thousands of high-availability servers worldwide. He pairs this low-level systems background with an applied AI product lens shaped by dual BS and MS degrees in Electrical and Computer Engineering from Carnegie Mellon University.

In addition to his operating roles, Saurabh has invested in and supported early-stage voice and AI startups through Google Assistant’s strategic investment programs, including seed and Series A bets in companies such as Instreamatic, Voiceflow, and Slang Labs. As a member of the Skip Community, he collaborates with a network of current and former heads of product who collectively bring hundreds of years of leadership experience across AI, fintech, cybersecurity, e-commerce, and renewable energy, shaping best practices for how modern product organizations are structured and scaled.

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Learn how a CPO at a billion-dollar AI company is rethinking what “good” looks like for PMs — prioritizing strategic thinking over feature-building as software commoditizes.

“You gotta be laser sharp about where you can really add value versus what’s being rapidly commoditized.”

Saurabh Sharma, CPO at You.com, doesn’t deliver this line as career advice. It’s operational reality. When AI can generate user research insights in minutes and prototype features faster than most teams can write specifications, the entire foundation of product management value shifts. The skills that made someone a great PM five years ago might make them unemployable five years from now.

“Where is there a compounding advantage? Where is there a value creation that will be hard to commoditize?” he continues, and I can see him working through the implications for his own hiring decisions. “And that’s a lot of what I think about at the company. That’s a lot of what I try to help my team think about as well.”

This isn’t abstract strategy. It’s survival math. You.com processes over a billion web search API queries per month for companies including DuckDuckGo, Windsurf, and Harvey. They raised $100 million at a $1.5 billion valuation. At that scale, every hiring decision carries weight. Every capability they build internally has to justify itself against what they could buy or automate.

The question Saurabh faces daily: when AI can handle most IC work, what human skills become more valuable rather than less?

“I think what’s really changed is you gotta be laser sharp about where you can really add value versus what’s being rapidly commoditized,” he explains. “And so I think what we’ve seen at You.com is that there’s a continuous focus on where is the value really being created versus where will the value be rapidly commoditized.”

The math is brutal but clarifying. If anyone can build a basic SaaS product with AI assistance, then building basic SaaS products isn’t a differentiating capability. If anyone can synthesize user research or analyze competitor data with AI tools, then those skills command lower wages and less organizational influence.

But here’s what Saurabh has observed: some capabilities become more valuable as their supporting infrastructure gets commoditized. Strategic judgment becomes more important when you can test more strategies. Pattern recognition becomes more critical when you have more data to parse. The ability to choose which problems are worth solving becomes essential when solving problems gets easier.

“And I think it does change how you hire, in that you want people that are able to think that strategic line more so than, well, here’s this cool feature I wanna build.”

The hiring implications ripple through every product organization. The PM who excels at writing detailed PRDs and coordinating feature launches might struggle in an environment where PRD writing is automated and feature quality is determined by rapid iteration rather than upfront specification.

But the PM who can identify which customer problems create sustainable advantage, who can spot market opportunities before competitors, who can build conviction around directions that don’t yet have validation—those skills compound as the tactical work gets easier.

“Well, the cool feature—the customer might be able to replicate it themselves in a way that’s even more fit for them,” Saurabh continues. “It’s more about where is there a compounding advantage? Where is there a value creation that will be hard to commoditize?”

I push him on this. How do you interview for strategic thinking? How do you distinguish between someone who talks strategically and someone who thinks strategically? Most product candidates can articulate frameworks and principles. Fewer can demonstrate judgment under uncertainty.

“I think that taking that more strategic approach, what separates a middle manager from an executive,” he responds, drawing a connection I didn’t expect. “Nobody told me that I should spend more time with the sales team. But what I noted was, first of all, sales likes having product on road trips with them. It helps customer conversations. But the other part of it was it helps me. It helps me build my worldview. What my roadmap should be.”

The example crystallizes the difference. Strategic thinking isn’t about having better frameworks or more elegant presentations. It’s about making connections that aren’t obvious, taking actions that aren’t prescribed, developing conviction through firsthand exploration rather than secondhand analysis.

When Saurabh decided to spend more time on sales calls, he wasn’t following a playbook. He was following a hunch about where his learning edge was. That hunch—and the willingness to act on it—represents the kind of judgment that becomes more valuable as tactical execution gets automated.

But this creates new tensions in how product teams operate. When strategic judgment becomes the scarce resource, how do you structure teams to maximize it? How do you delegate the increasing scope of work that AI can handle without losing touch with the details that inform strategy?

“None of us are gonna be ICs anymore,” Saurabh says, quoting You.com CEO Richard Socher. “We are all gonna be managers in the future. Some of us will continue to manage people, but your traditional IC will now be managing a fleet of agents that’s doing a lot of work for them.”

The transition from IC to manager isn’t just about career advancement. It’s about cognitive load distribution. When AI can handle research, analysis, and initial synthesis, human intelligence gets freed up for higher-order work: choosing which questions to ask, interpreting ambiguous signals, making bets on uncertain outcomes.

But managing AI agents requires different skills than managing humans. Humans can fill in context, interpret vague instructions, escalate when they’re confused. AI agents do exactly what you ask them to do, which means the quality of your instructions determines the quality of their output.

“Many of the emails I write, I will pass through AI to help me with tone or help me think about the way I want to get to a particular objective in a given customer situation,” he explains, describing his own evolution. “That is essentially an example of offloading something that we all know how to do. I could write that perfect email to a customer to diffuse a complex situation, but it might take me an hour to really think through it and get it right. What I found is that email is now five minutes away working with AI.”

The email example is tactical, but the implications are strategic. When routine communication becomes effortless, you can maintain relationships at scale that were previously impossible. When difficult conversations can be crafted quickly, you can engage in more of them. The scope of what one person can manage expands dramatically.

This expansion creates competitive advantage for individuals and organizations that adapt quickly. But it also creates new forms of inequality. People who learn to manage AI agents effectively can take on exponentially more responsibility. People who don’t learn these skills find their scope of influence shrinking as AI-augmented colleagues outpace them.

“Some people will use the time that they get back with AI to just do more of what they already know, and that’s gonna be fine,” Saurabh observes. “But you’re gonna have other people that are able to—I sometimes think about Maslow’s hierarchy. Some people that are able to, okay, great, I got shelter and food under control. Now I can go to self-actualization.”

The Maslow reference isn’t casual. It’s how he thinks about organizational development in an AI-augmented world. Some people will use AI to get better at their current job. Others will use AI to access entirely different kinds of work. The first group maintains their position. The second group expands their influence.

But this creates new challenges for team composition. How do you balance strategic thinkers who can direct AI agents effectively with craftspeople who can execute at high quality? How do you maintain institutional knowledge when so much tactical work gets delegated to machines?

“There are exceptional middle managers that that’s what they love to do. That’s what they’re good at, and that is great,” Saurabh says when I ask about the career implications. “And then there are exceptional middle managers that graduate naturally to be exceptional executives. And that is good as well.”

The key insight: both paths remain valuable, but the skills required for each path are changing. Middle managers will increasingly manage hybrid teams of humans and AI agents. They’ll need to be excellent at coordination, quality control, and tactical execution within defined boundaries. Executives will set those boundaries, choose which problems deserve attention, and build conviction around uncertain directions.

But the boundary between these roles is becoming more porous. When AI handles routine analysis, middle managers can engage in more strategic work. When strategic insights can be tested rapidly, executives can stay closer to tactical details. The rigid hierarchies built around information scarcity start to flatten when information becomes abundant.

“Where is that compounding advantage that creates value for the customer and also creates potentially a competitive moat for us as well,” Saurabh concludes, returning to the core question.

The answer, increasingly, isn’t in what you can build. It’s in what you choose to build and why. The technical capability to create software is becoming commoditized. The judgment to create the right software at the right time for the right customers remains scarce.

Companies that hire for execution speed will compete on efficiency. Companies that hire for strategic judgment will compete on alpha. Both approaches can succeed, but they require different organizational designs and different definitions of performance.

The retailers who miss the next pickleball trend won’t be the ones with outdated technology stacks. They’ll be the ones who couldn’t distinguish between signals worth pursuing and noise worth ignoring. Who couldn’t move fast enough from insight to action. Who optimized for doing more of the same instead of doing something different.

“I think what we’ve seen at You.com is that there’s a continuous focus on where is the value really being created versus where will the value be rapidly commoditized.”

As AI makes more capabilities available to everyone, the companies that thrive will be the ones that focus obsessively on the capabilities that can’t be commoditized. Not because they’re technically difficult, but because they require the kind of human judgment that compounds over time rather than getting automated away.

The question for every product organization: are you hiring people who can do the work, or people who can choose the work? The first skill set has a shrinking shelf life. The second becomes more valuable every sprint.

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Anya Cheng is the Founder and CEO of Taelor, an AI-powered menswear rental and styling platform at the intersection of fashion, data, and artificial intelligence. Rising to prominence in the 2010s after leading product teams at Meta, eBay, Target, and McDonald’s, she became known for scaling digital products that touched hundreds of millions of users while bridging consumer behavior, growth, and personalization. Today she is widely regarded as an influential figure in fashion tech and serves as faculty at Northwestern University, translating operating experience into curriculum on integrated marketing and product strategy.

Previously, as a senior product leader at Meta, eBay, Target, and McDonald’s, she owned global initiatives that drove measurable business outcomes across eCommerce, food delivery, and retail. At McDonald’s she helped lead the global rollout of mobile ordering to thousands of stores, transforming how customers interacted with a brand serving more than 60 million people per day. At Taelor, her team has raised approximately $2.3 million in pre-seed funding, achieved over 10 million marketing impressions with zero ad budget, and earned recognition such as Inc.’s 2025 Best in Business – Best Startup category and Webby Award honors.

Her career highlights include award‑winning marketing campaigns at Sears and Kmart, scaling cross‑border digital commerce at eBay, and driving omnichannel experiences at Target that combined stores, mobile, and online into a unified customer journey. As founder of Taelor, she has built an AI-driven styling engine that mixes acquired competitor data, human stylists, and feedback loops from thousands of garment rentals to improve recommendations and reduce fashion waste. Along the way she has been named to Girls in Tech’s “40 Under 40,” delivered a TEDx talk on perseverance, and built a following of more than 28,000 professionals who track her work across AI, circular fashion, and consumer technology.

As a book author, startup advisor, and frequent podcast guest, Cheng documents the path from Taiwan to Silicon Valley and distills lessons on resilience, go‑to‑market execution, and human‑centered AI. As a teacher at Northwestern University and a sought‑after speaker at industry events like NRF and SF Tech Week, she helps the next generation of founders and operators understand how to turn data, storytelling, and product intuition into enduring companies.

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The framework Meta uses in PM interviews to separate great product thinkers from idea generators.

“Nobody used the feature besides a product manager,” Anya Cheng tells me. “Why?”

She’s describing a project from her time at Target. The team wanted to build store GPS—beacon-powered navigation so customers would never forget an item on their list. They spent six months and millions of dollars mapping every item location in stores with different layouts and footprints. They geo-fenced the shelves. They built the feature. They launched it.

“Come on,” she says. “Mom is going to a Target store to get lost. They want to go to a store wandering around and buy stuff.”

The Target moms didn’t need efficiency. They needed escape. The Starbucks inside is the feature. The cup holders on the cart are the feature. The permission to wander for an hour away from noisy kids is the feature. The team had solved the wrong problem perfectly.

Anya Cheng is the founder and CEO of Taelor, an AI-powered menswear rental subscription. Before founding Taelor she was Head of Product at Meta for Facebook and Instagram Shopping, Head of Product at eBay for Latin America and Africa, led mobile and tablet e-commerce at Target, and was Senior Director at McDonald’s launching their global food delivery apps. She teaches product management at Northwestern and has won 20-plus industry awards. The Target GPS story is one she uses to teach the most important lesson she knows: the quality of your execution is irrelevant if you’re solving the wrong problem.

“If you are taking away the value prop,” she says, “then your product is just not going to be popular.”

Target’s value proposition isn’t convenience. It’s discovery. It’s the opposite of a GPS. The beacon team understood the technology. They understood the implementation challenge. They just didn’t understand why moms go to Target.

I ask Anya how she avoids the same trap. How she decides what to build and—more importantly—what not to build. Her answer is a framework she’s used at Meta, eBay, McDonald’s, and now Taelor.

It starts with the Facebook PM interview question: if you’re the product manager of X, what feature would you launch? She’s been on both sides of this question hundreds of times. The candidates who fail are the ones who answer it.

“Two types of person,” she says. “One type will be out of the interview loop right away. The other will at least get to the second level.”

The first type jumps to solutions. I’d build this, I’d build that. Ideas are cheap. ChatGPT can come up with ideas. That’s not the job.

The second type starts with personas. She gives me the birthday product example. Three personas: the birthday person who wants to be surprised, the close friends who want to organize and are afraid of forgetting, and the acquaintances who just want to say happy birthday. Each has distinct pain points. Each pain point sits on a spectrum of severity, frequency, and relevance to Facebook’s unique position.

“Then you come up with selecting criteria,” Anya says. “Which pain point is more painful? Which pain point has more people with that pain point? Which pain point is Facebook more relevant to solving versus other people?”

The criteria filter the problem space before you ever touch solutions. Then when you do generate solutions, you filter again: which solution solves the problem best, which takes fewer engineering hours, which fits the direction of the business?

“Up to here,” she says, “I haven’t told you anything about the solution.”

She brings up the same framework when she tells me about Google Shopping versus Facebook Shopping. Same goal: sell things online. Completely different products. Google’s mission is organizing the world’s information, so Google Shopping became price comparison. Meta’s mission is bringing the world closer together, so Facebook Shopping became community commerce—friends selling bicycles from their backyard, influencers sharing product recommendations.

“Exactly the same goal,” she says. “But totally different product because it’s different mission of the company.”

The mission is the highest-level selection criterion. It determines which problems are yours to solve and which aren’t. The Target beacon team forgot this. They selected a problem—moms forgetting items—that was real but irrelevant to why people went to Target in the first place.

Anya’s own origin story follows the framework precisely. At Meta, she was dealing with imposter syndrome—a Taiwanese immigrant surrounded by Ivy League engineers. She needed to look good. She tried Stitch Fix (had to buy everything), Rent the Runway (had to browse 100,000 garments). She realized fashion companies designed for fashion lovers, not for people who wanted to get ready and get on with their day.

So she did product 101. Interviewed people. Found that her real persona wasn’t women like her—it was busy men. Sales guys, consultants, pastors, executives. People who didn’t care about fashion but cared deeply about the outcomes fashion enabled: getting a job, closing a deal, landing a date.

The MVP was a Shopify landing page with a stock photo of blue shorts. A realtor from San Diego put his email in, waited two months, found Anya on LinkedIn, and called her. They bought clothes from Macy’s during a Christmas sale and shipped from the post office.

“Became our first customer,” she says. “The MVP still worked.”

It worked because the hypothesis was right. The problem was real. The selection criteria—not the solution—validated the business. Everything that followed—the 150 brand partnerships, the AI-augmented styling, the circular fashion model—was built on the foundation of understanding what the customer actually needed.

She tells me about another failed product: eBay’s AI-powered listing tool. Snap a photo of a bicycle, AI writes the description. Built it. Shipped it. Nobody used it. Small sellers on eBay have sentimental attachment to their items. They want to write their own descriptions. Efficiency wasn’t the pain point. Pride was.

“If you don’t deeply understand the customer persona, the insider psychology, the job to be done,” she says, “it’s just very hard to build a great product.”

I bring up vibe coding—the trend of PMs building functional prototypes with AI tools on weekends. Her intern did exactly this: came back with three working features built in a weekend. Her response was blunt.

“This is how exactly at Meta we don’t hire people.”

The features might have been good. But they were selected by enthusiasm, not criteria. The intern skipped the framework—the personas, the pain points, the filtering—and went straight to building. AI made it possible to skip the hard work. And skipping the hard work is exactly the failure mode that produces Target store GPS.

“In the old time,” Anya says, “you have three ideas and you have to go convince your engineer and designer. And they will challenge your logic. But now you can skip all of this.”

The challenge was the quality filter. Removing it doesn’t make you faster. It makes you wrong more efficiently.

I ask Anya what she wants product leaders to take away from all of this. She doesn’t hesitate.

“We are all problem solvers,” she says. “Go to the meeting. Forget that you are a designer, forget that you are PM, and really focus on thinking about what problem can be solved.”

The solutions will come. They always do. The hard part—the part that separates a Target beacon from a Taelor, a failed eBay listing tool from a 10-million-impression marketing flywheel—is choosing the right problem in the first place. Not the coolest one. Not the most technically interesting one. The one that actually matters to the person on the other end.

Selection criteria over ideas. Every time.

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Maxine Anderson is the Co-founder and Chief Product Officer at Arist, where she helps build what is widely regarded as an emerging default enablement system for large enterprises. Rising to prominence in the early 2020s, she became known for transforming text-message learning experiments into an agentic enablement platform that operates directly inside Slack, Microsoft Teams, and SMS. Under her product leadership, Arist has evolved from simple SMS-based courses to an AI-driven “enablement team in your pocket” that automates needs analysis, content creation, and delivery for distributed workforces at scale.

Previously, as Co-founder and Chief Product Officer at Arist, Anderson helped expand the company’s initial seed funding to $3.9 million in 2021 and later raise a $12 million Series A round to fuel rapid enterprise adoption. Her work turned an early Y Combinator-backed idea into a venture serving over 20 Fortune 500 organizations, with pricing starting around $1,000 per month for enterprise deployments. She became known for shipping AI-powered tools such as Creator and the Enablement Agent, which process thousands of complex documents, translate into 100+ languages, and generate ready-to-deliver programs in under eight minutes while proving impact through end-to-end analytics.

Her career highlights include co-founding Project W, a student-led organization launched in 2021 to foster interdisciplinary collaboration among women innovators and entrepreneurs across the Babson, Olin, and Wellesley (BOW) colleges, which built an online community of more than 300 members and incubated Project Pods for high-level ventures. As a founding member of College Ventures Network and VP of Marketing at eTower, Babson’s premier entrepreneurial living community whose alumni companies have generated more than $3 billion in combined valuations and over $50 million in funding, she honed a model for building tight-knit entrepreneurial ecosystems. Graduating magna cum laude from Babson College in 2022 with a focus on entrepreneurship, she combined academic honors with hands-on leadership roles that emphasized measurable impact and community scale.

Outside of her primary operating role, Anderson serves as a Board Member at Delphian School, bringing startup execution and product thinking back into the education system where she was once Student Council President and a three-time state champion cheerleading captain. Through ongoing advisory work and public writing on enablement, AI agents, and performance diagnostics, she has become an influential figure for operators building the next generation of enterprise learning and HR technology.

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How Arist navigated seven years of positioning iteration in an undefined category and why shared conviction about the game you’re playing gives product the agency to say no.

“We are a new category without ever having created or yet created a category, which is hard to sell,” Maxine Anderson says. There’s no frustration in it. Just the accumulated weight of seven years spent explaining something that doesn’t have a name.

Maxine is the co-founder and CPO of Arist, a platform that delivers employee training through Microsoft Teams, SMS, and WhatsApp instead of video-based learning management systems. She started the company at Babson College with two co-founders after they each independently discovered that text-based communication drove behavior change in ways traditional mediums couldn’t.

The student in Yemen who could only learn via text. The public speaking coach who sent WhatsApp reminders before talks. Maxine’s own financial literacy programs on Native American reservations where classroom formats failed completely. The insight was simple. The seven years that followed were not.

I ask her about positioning, and the answer is a catalog of pivots. They started as a consumer marketplace—Masterclass over text, basically. Learn from professors at Harvard via your phone. “That model was just really hard to distribute,” she says. “Marketplaces are just really difficult, to be honest. Not really good for a medium that people didn’t already believe in.”

A former chief learning officer told them about the billions spent on corporate training that drove zero results. They pivoted to corporate learning. Spent two years selling to HR. Got traction—then the market shifted. Enterprise budgets contracted in 2021 and 2022, and HR was the first department cut.

“It was kind of a forcing function for us to find a better buyer,” Maxine says.

They started selling to operational leaders. Sales directors. Frontline manufacturing managers. People whose bonuses depended on whether their teams improved. The product hadn’t changed much. The positioning had changed completely.

I tell Maxine this is the part of product strategy that I think most product leaders miss. It isn’t about filling up a backlog and deciding which features will close deals. It’s figuring out what game you’re playing. There’s a great piece—I think it’s an a16z blog—about how the market is the most important thing. You can change your positioning and your target segment and sales go up. You don’t have to add more features.

“Yeah,” she says. “We’ve had to iterate on our positioning a lot.”

She describes what it’s like to sell without a category. Not just positioning on a macro level—telling the market a new way of thinking about employee enablement—but positioning per account. Every conversation is a custom pitch. Every buyer needs to understand something that doesn’t map to any existing line item in their budget.

“For a while it was hard to lead product,” she admits. “We’re selling all these different use cases yet we don’t want to productize those pathways. We’re not a sales enablement tool. We’re not trying to compete with HighSpot directly. We’re really good for this part of sales enablement, this problem that’s not solved.”

I bring up Figma as a parallel. How long it took for Figma to convince designers to switch from their existing tools. How category change requires not just a better product but a change in default behavior.

“It did take a long time for Figma to get traction,” she agrees. “They had to change people from their default behavior of going to other tools as a solution.”

The conversation moves to roadmap, and Maxine lights up. “There’s this quote that I love,” she says. “Plans are useless, but planning is useful. And I feel like that’s really true in a startup.”

She describes the trap she sees product managers fall into: optimizing for delivery. Presenting a roadmap, hitting dates, feeling the satisfaction of shipping what you said you’d ship. She says the feeling of executing on a plan is seductive—and often wrong.

“A roadmap often becomes a ton of things people ask for instead of what you’re trying to build towards over time,” she says. “Some of our best features have been where it doesn’t feel good. We shipped this a little too early, or we shipped this to see if we could market it. Or we marketed this five months early and built it in a funny way.”

This is the part where most product conversations would veer into framework territory. Maxine stays concrete. She describes how she segments her roadmap into three buckets: what they’re working towards building, what they’re trying to build to convince people, and what they’re building because it’s literally blocking adoption at scale.

“Those are the customer requests I take,” she says. “Literally, we would have five times volume if we shipped this feature. Not—oh, I would really love it if you could add this to a course.”

She confesses they fell into the feature parity trap early. Customers would compare Arist to existing LMS products. The team spent six months adding features that mapped to what learning management systems already had—instead of building the fundamentally different thing they were supposed to be building.

“What we’re building is fundamentally so different,” she says. “I have the agency in meetings with executives to say—that’s actually not our perspective. This is what we’re trying to build. This is what enablement should look like in five years, trust us. And it makes them back off a little bit.”

That agency comes from conviction. Not confidence—conviction. Knowing what game you’re playing well enough to explain why certain features will never be built. Maxine tells me she spent significant time enabling the entire company on Arist’s vision. Not just the product team. Everyone. So that when a salesperson gets a feature request in the field, they can explain why Arist won’t build a one-on-one coaching product, and here’s why, and they will never build that, and here’s why.

“Them being able to say those things is super valuable,” she says. “Because then you don’t get all these incoming requests of product to manage.”

I ask whether finding the right buyer helped with breathing room for product.

“Market is everything for product,” she says. Four words. No hedging.

Finding the right buyer improved retention, simplified the roadmap, reduced internal pressure. It did what no process improvement or planning framework ever could: it gave product permission to build the right thing.

Her co-founder, she tells me, is the one who holds the macro stance. “It’s very easy in a business to just really want the wins and explain things in ways people understand,” she says. “It takes a lot of positioning iteration to stick to the macro.”

She mentions other companies in adjacent spaces that built text-message learning tools but positioned them as utilities for learning designers. They don’t see that learning designers won’t exist in their current form three years from now. They’re solving for today’s buyer in today’s category. Arist is building for a category that doesn’t exist yet.

“It does require someone who takes the right macro bets,” Maxine says. “Which you need someone who can do that well.”

I think about Linear’s five-year slide—year one is friends, year two is small startups, year five is enterprise. The CPO who defaults to no on dashboard requests because they’re counter-positioning against Atlassian. The clarity that comes not from better planning but from sharper conviction about who you’re building for.

Maxine and her co-founder have that clarity. It took seven years of positioning iteration, a near-shutdown, a global pandemic, and the courage to walk away from the HR buyer. But they have it. And the roadmap, as she predicted, is taking care of itself.

The Way of Product w/ Caden Damiano is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.